Untitled Essay, Research Paper

ABSTRACTCurrent neural network technology is the most progressive of the artificial intelligence

systems today. Applications of neural networks have made the transition from laboratory

curiosities to large, successful commercial applications. To enhance the security of

automated

financial transactions, current technologies in both speech recognition and handwriting

recognition are likely ready for mass integration into financial institutions.RESEARCH PROJECT

TABLE OF CONTENTS

Introduction 1

Purpose 1

Source of Information 1

Authorization 1

Overview 2

The First Steps 3

Computer-Synthesized Senses 4

Visual Recognition 4

Current Research 5

Computer-Aided Voice Recognition 6

Current Applications 7

Optical Character Recognition 8

Conclusion 9

Recommendations 10

Bibiography 11

INTRODUCTION? Purpose The purpose of this study is to determine additional areas where

artificial intelligence

technology may be applied for positive identifications of individuals

during financial

transactions, such as automated banking transactions, telephone

transactions , and home

banking activities. This study focuses on academic research in neural

network technology .

This study was funded by the Banking Commission in its effort to deter

fraud.

Overview Recently, the thrust of studies into practical applications for

artificial intelligence

have focused on exploiting the expectations of both expert systems and

neural network

computers. In the artificial intelligence community, the proponents of

expert systems

have approached the challenge of simulating intelligence differently

than their counterpart

proponents of neural networks. Expert systems contain the coded

knowledge of a human expert

in a field; this knowledge takes the form of "if-then" rules.

The problem with this approach

is that people don’t always know why they do what they do. And

even when they can express this

knowledge, it is not easily translated into usable computer code. Also,

expert systems are

usually bound by a rigid set of inflexible rules which do not change

with experience gained

by trail and error. In contrast, neural networks are designed around

the structure of a

biological model of the brain. Neural networks are composed of simple

components called

"neurons" each having simple tasks, and simultaneously

communicating with each other by

complex interconnections. As Herb Brody states, "Neural networks

do not require an explicit

set of rules. The network – rather like a child – makes up its own

rules that match the

data it receives to the result it’s told is correct" (42).

Impossible to achieve in expert

systems, this ability to learn by example is the characteristic of

neural networks that makes

them best suited to simulate human behavior. Computer scientists have

exploited this system

characteristic to achieve breakthroughs in computer vision, speech

recognition, and optical

character recognition. Figure 1 illustrates the knowledge structures of

neural networks

as compared to expert systems and standard computer programs. Neural

networks restructure

their knowledge base at each step in the learning process.

This paper focuses on neural network technologies which have the

potential to increase security

for financial transactions. Much of the technology is currently in the

research phase and has

yet to produce a commercially available product, such as visual

recognition applications.

Other applications are a multimillion dollar industry and the products

are well known, like

Sprint Telephone’s voice activated telephone calling system. In

the Sprint system the neural

network positively recognizes the caller’s voice, thereby

authorizing activation of his

calling account.

The First Steps The study of the brain was once limited to the study of living tissue.

Any attempts at an

electronic simulation were brushed aside by the neurobiologist

community as abstract conceptions

that bore little relationship to reality. This was partially due to the

over-excitement in

the 1950’s and 1960’s for networks that could recognize some

patterns, but were limited in

their learning abilities because of hardware limitations. In the 1990’s

computer simulations

of brain functions are gaining respect as the simulations increase

their abilities to predict

the behavior of the nervous system. This respect is illustrated by the

fact that many

neurobiologists are increasingly moving toward neural network type

simulations. One such

neurobiologist, Sejnowski, introduced a three-layer net which has made

some excellent predictions

about how biological systems behave. Figure 2 illustrates this network

consisting of three

layers, in which a middle layer of units connects the input and output

layers. When the network

is given an input, it sends signals through the middle layer which

checks for correct output.

An algorithm used in the middle layer reduces errors by strengthening

or weakening connections

in the network. This system, in which the system learns to adapt to the

changing conditions,

is called back-propagation. The value of Sejnowski’s network is

illustrated by an experiment

by Richard Andersen at the Massachusetts Institute of Technology.

Andersen’s team spent years

researching the neurons monkeys use to locate an object in space

(Dreyfus and Dreyfus 42-61).

Anderson decided to use a neural network to replicate the findings from

their research. They

"trained" the neural network to locate objects by retina and

eye position, then observed

the middle layer to see how it responded to the input. The result was

nearly identical to what

they found in their experiments with monkeys.Computer-Synthesized Senses

? Visual Recognition

The ability of a computer to distinguish one customer from another is

not yet a reality. But, recent breakthroughs in neural network visual technology are

bringing us closer to the time when computers will positively identify a person.

? Current Research Studying the retina of the eye is the focus of research by two

professors at the California

Institute of Technology, Misha A. Mahowald and Carver Mead. Their

objective is to electronically

mimic the function of the retina of the human eye. Previous research in

this field consisted

of processing the absolute value of the illumination at each point on

an object, and required

a very powerful computer.(Thompson 249-250). The analysis required

measurements be taken over

a massive number of sample locations on the object, and so, it required

the computing power of a

massive digital computer to analyze the data.

The professors believe that to replicate the function of the human

retina they can use a neural

network modeled with a similar biological structure of the eye, rather

than simply using massive

computer power. Their chip utilizes an analog computer which is less

powerful than the previous

digital computers. They compensated for the reduced computing power by

employing a far more

sophisticated neural network to interpret the signals from the

electronic eye. They modeled the

network in their silicon chip based on the top three layers of the

retina which are the best

understood portions of the eye.(250) These are the photoreceptors,

horizontal cells, and bipolar cells.

The electronic photoreceptors, which make up the first layer, are like

the rod and cone cells in the eye.

Their job is to accept incoming light and transform it into electrical

signals. In the second

layer, horizontal cells use a neural network technique by

interconnecting the horizontal cells

and the bipolar cells of the third layer. The connected cells then

evaluate the estimated

reliability of the other cells and give a weighted average of the

potentials of the cells

around it. Nearby cells are given the most weight and far cells less

weight.(251)

This technique is very important to this process because of the dynamic

nature of image

processing. If the image is accepted without testing its probable

accuracy, the likelihood

of image distortion would increase as the image changed.

The silicon chip that the two professors developed contains about 2,500

pixels— photoreceptors

and their associated image-processing circuitry. The chip has circuitry

that allows a professor

to focus on each pixel individually or to observe the whole scene on a

monitor. The professors

stated in their paper, "The behavior of the adaptive retina is

remarkably similar to that of

biological systems" (qtd in Thompon 251). The retina was first tested by changing the light intensity of just one

single pixel while the

intensity of the surrounding cells was kept at a constant level. The

design of the neural network

caused the response of the surrounding pixels to react in the same

manner as in biological retinas.

They state that, "In digital systems, data and computational

operations must be converted into

binary code, a process that requires about 10,000 digital voltage

changes per operation.

Analog devices carry out the same operation in one step and so decrease

the power consumption

of silicon circuits by a factor of about 10,000" (qtd in Thompson

251).

Besides validating their neural network, the accuracy of this silicon

chip displays the usefulness

of analog computing despite the assumption that only digital computing

can provide the accuracy

necessary for the processing of information.

As close as these systems come to imitating their biological

counterparts, they still have a long

way to go. For a computer to identify more complex shapes, e. g., a

person’s face, the professors

estimate the requirement would be at least 100 times more pixels as

well as additional circuits

that mimic the movement-sensitive and edge-enhancing functions of the

eye. They feel it is possible

to achieve this number of pixels in the near future. When it does

arrive, the new technology will

likely be capable of recognizing human faces.

Visual recognition would have an undeniable effect on reducing crime in

automated financial transactions.

Future technology breakthroughs will bring visual recognition closer to

the recognition of individuals,

thereby enhancing the security of automated financial transactions.? Computer-Aided Voice Recognition Voice recognition is another area that has been the subject of neural

network research.

Researchers have long been interested in developing an accurate

computer-based system capable

of understanding human speech as well as accurately identifying one

speaker from another.

? Current Research Ben Yuhas, a computer engineer at John Hopkins University, has

developed a promising system for

understanding speech and identifying voices that utilizes the power of

neural networks. Previous attempts

at this task have yielded systems that are capable of recognizing up to

10,000 words, but only when each

word is spoken slowly in an otherwise silent setting. This type of

system is easily confused by back

ground noise (Moyne 100).

Ben Yuhas’ theory is based on the notion that understanding human

speech is aided, to some small degree,

by reading lips while trying to listen. The emphasis on lip reading is

thought to increase as the

surrounding noise levels increase. This theory has been applied to

speech recognition by adding a

system that allows the computer to view the speaker’s lips through

a video analysis system while

hearing the speech.

The computer, through the neural network, can learn from its mistakes

through a training session. Looking

at silent video stills of people saying each individual vowel, the

network developed a series of

images of the different mouth, lip, teeth, and tongue positions. It

then compared the video images

with the possible sound frequencies and guessed which combination was

best.

Yuhas then combined the video recognition with the speech recognition

systems and input a video frame

along with speech that had background noise. The system then estimated

the possible sound frequencies

from the video and combined the estimates with the actual sound

signals. After about 500 trial runs the

system was as proficient as a human looking at the same video

sequences.

This combination of speech recognition and video imaging substantially

increases the security factor by

not only recognizing a large vocabulary, but also by identifying the

individual customer using the system.? Current Applications Laboratory advances like Ben Yuhas’ have already created a

steadily increasing market in speech recognition.

Speech recognition products are expected to break the billion-dollar

sales mark this year for the first time.

Only three years ago, speech recognition products sold less than $200

million (Shaffer, 238).

Systems currently on the market include voice-activated dialing for

cellular phones, made secure by their

recognition and authorization of a single approved caller.

International telephone companies such as Sprint

are using similar voice recognition systems. Integrated Speech Solution

in Massachusetts is investigating

speech applications which can take orders for mutual funds prospectuses

and account activities (239).? Optical Character Recognition Another potential area for transaction security is in the

identification of handwriting by optical

character recognition systems (OCR). In conventional OCR systems the

program matches each letter in a

scanned document with a pre-arranged template stored in memory. Most

OCR systems are designed specifically

for reading forms which are produced for that purpose. Other systems

can achieve good results with

machine printed text in almost all font styles. However, none of the

systems is capable of recognizing

handwritten characters. This is because every person writes

differently.

Nestor, a company based in Providence, Rhode Island has developed

handwriting recognition products based

on developments in neural network computers. Their system,

NestorReader, recognizes handwritten characters

by extracting data sets, or feature vectors, from each character. The

system processes the input

representations using a collection of three by three pixel edge

templates (Pennisi, 23). The system then

lays a grid over the pixel array and pieces it together to form a

letter. Then the network discovers

which letter the feature vector most closely matched. The system can

learn through trial and error,

and it has an accuracy of about 80 percent. Eventually this system will

be able to evaluate all symbols

with equal accuracy.

It is possible to implement new neural-network based OCR systems into

standard large optical systems.

Those older systems, used for automated processing of forms and

documents, are limited to reading typed

block letters. When added to these systems, neural networks improve

accuracy of reading not only typed

letters but also handwritten characters. Along with automated form

processing, neural networks will

analyze signatures for possible forgeries.

Conclusion Neural networks are still considered emerging technology and have a

long way to go toward achieving their

goals. This is certainly true for financial transaction security. But

with the current capabilities,

neural networks can certainly assist humans in complex tasks where

large amounts of data need to be analyzed.

For visual recognition of individual customers, neural networks are

still in the simple pattern matching

stages and will need more development before commercially acceptable

products are available. Speech

recognition, on the other hand, is already a huge industry with

customers ranging from individual computer

users to international telephone companies. For security, voice

recognition could be an added link to the

chain of pre-established systems. For example, automated account

inquiry, by telephone, is a popular method

for customers to determine the status of existing accounts. With voice

identification of customers, an

option could be added for a customer to request account transactions

and payments to other institutions.

For credit card fraud detection, banks have relied on computers to

identify suspicious transactions.

In fraud detection, these programs look for sudden changes in spending

patterns such as large cash withdrawals

or erratic spending. The drawback to this approach is that there are

more accounts flagged for possible

fraud than there are investigators. The number of flags could be

dramatically reduced with optical character

recognition to help focus investigative efforts.

It is expected that the upcoming neural network chips and add-on boards

from Intel will add blinding speed

to the current network software. These systems will even further reduce

losses due to fraud by enabling

more data to be processed more quickly and with greater accuracy.

Recommendations

Breakthroughs in neural network technology have already created many

new applications in financial transaction

security. Currently, neural network applications focus on processing

data such as loan applications, and

flagging possible loan risks. As computer hardware speed increases and

as neural networks get smarter,

"real-time" neural network applications should become a

reality. "Real-time" processing means the network

processes the transactions as they occur.

In the mean time,

1. Watch for advances in visual recognition hardware / neural networks.

When available, commercially produced

visual recognition systems will greatly enhance the security of

automated financial transactions.2. Computer aided voice recognition is already a reality. This

technology should be implemented in automated

telephone account inquiries. The feasibility of adding phone

transactions should also be considered.

Cooperation among financial institutions could result in secure

transfers of funds between banks when

ordered by the customers over the telephone.3. Handwriting recognition by OCR systems should be combined with

existing check processing systems.

These systems can reject checks that are possible forgeries.

Investigators could follow-up on the

OCR rejection by making appropriate inquiries with the check writer.

BIBLIOGRAPHYWinston, Patrick. Artificial Intelligence. Menlo Park: Addison-Wesley Publishing, 1988.Welstead, Stephen. Neural Network and Fuzzy Logic in C/C++. New York: Welstead, 1994.Brody, Herb. "Computers That Learn by Doing." Technology Review August 1990:

42-49.Thompson, William. "Overturning the Category Bucket." BYTE January 1991:

249-50+.Hinton, Geoffrey. "How Neural Networks Learn from Experience." Scientific

American September 1992: 145-151.Dreyfus, Hubert., and Stuart E. Dreyfus. "Why Computers May Never Think Like

People." Technology Review January 1986: 42-61.Shaffer, Richard. "Computers with Ears." FORBES September 1994: 238-239.